Merge pull request #447 from kuke/adapt_tuning

Adapt tuning script to padding removing #444

deep_speech_2/tools/tune.py

@@ -88,18 +88,34 @@ def tune():
         augmentation_config='{}',
         specgram_type=args.specgram_type,
         num_threads=args.num_proc_data,
-        keep_transcription_text=True)
+        keep_transcription_text=True,
+        num_conv_layers=args.num_conv_layers)
 
     audio_data = paddle.layer.data(
         name="audio_spectrogram",
         type=paddle.data_type.dense_array(161 * 161))
     text_data = paddle.layer.data(
         name="transcript_text",
         type=paddle.data_type.integer_value_sequence(data_generator.vocab_size))
+    seq_offset_data = paddle.layer.data(
+        name='sequence_offset',
+        type=paddle.data_type.integer_value_sequence(1))
+    seq_len_data = paddle.layer.data(
+        name='sequence_length',
+        type=paddle.data_type.integer_value_sequence(1))
+    index_range_datas = []
+    for i in xrange(args.num_rnn_layers):
+        index_range_datas.append(
+            paddle.layer.data(
+                name='conv%d_index_range' % i,
+                type=paddle.data_type.dense_vector(6)))
 
     output_probs, _ = deep_speech_v2_network(
         audio_data=audio_data,
         text_data=text_data,
+        seq_offset_data=seq_offset_data,
+        seq_len_data=seq_len_data,
+        index_range_datas=index_range_datas,
         dict_size=data_generator.vocab_size,
         num_conv_layers=args.num_conv_layers,
         num_rnn_layers=args.num_rnn_layers,
@@ -156,15 +172,17 @@ def tune():
     for infer_data in batch_reader():
         if (args.num_batches >= 0) and (cur_batch >= args.num_batches):
             break
-        infer_results = inferer.infer(input=infer_data)
-
-        num_steps = len(infer_results) // len(infer_data)
+        infer_results = inferer.infer(input=infer_data,
+                                      feeding=data_generator.feeding)
+        start_pos = [0] * (len(infer_data) + 1)
+        for i in xrange(len(infer_data)):
+            start_pos[i + 1] = start_pos[i] + infer_data[i][3][0]
         probs_split = [
-            infer_results[i * num_steps:(i + 1) * num_steps]
-            for i in xrange(len(infer_data))
+                infer_results[start_pos[i]:start_pos[i + 1]]
+                for i in xrange(0, len(infer_data))
         ]
 
-        target_transcripts = [transcript for _, transcript in infer_data]
+        target_transcripts = [ data[1] for data in infer_data ]
 
         num_ins += len(target_transcripts)
         # grid search